Department of Chemistry and Research Institute for Convergence of Basic Science, Hanyang University, Seoul, 04763, Republic of Korea.
Department of Chemistry, Institute for Molecular Science and Fusion Technology, Kangwon National University, Chunchon, 24341, South Korea.
Talanta. 2020 May 15;212:120748. doi: 10.1016/j.talanta.2020.120748. Epub 2020 Jan 15.
A strategy of combining temperature-induced spectral variation and two-dimensional correlation (2D-COS) analysis as a potential tool to improve accuracy of sample discrimination is suggested. The potential application of this method was evaluated using near-infrared (NIR) spectroscopic discrimination of adulterated olive oils. Rather than utilizing static spectral information at a certain temperature, dynamic spectral features induced by an external perturbation such as temperature change would be more informative for sample discrimination, and 2D-COS analysis was a reliable choice to characterize temperature-induced spectral variation. For evaluation, NIR spectra of 9 pure olive oils and 90 olive oils adulterated with canola, soybean, and corn oils (adulteration rate: 5%) were collected at four different temperatures (20, 27, 34, 41 °C). In constant-temperature measurements, the scores of pure and adulterated samples obtained by principal component analysis (PCA) were considerably overlapped. When 2D-COS analysis was performed using temperature-varied (20-41 °C) spectra and the resulting power spectra from 2D synchronous correlation spectra were used for PCA, identification of the two groups was noticeably enhanced and subsequent k-nearest neighbor (k-NN)-based discrimination accuracy substantially improved to 86.4%. While, the accuracies resulted in the constant-temperature measurements ranged only from 50.9 to 55.8%. The dynamic temperature-induced spectral variation of the samples effectively featured by 2D-COS analysis was ultimately more informative and allowed improvement in accuracy.
提出了一种将温度诱导的光谱变化和二维相关（2D-COS）分析相结合的策略，作为提高样品鉴别准确性的潜在工具。该方法的潜在应用通过近红外（NIR）光谱鉴别掺假橄榄油进行了评估。与在特定温度下利用静态光谱信息不同，外部扰动（如温度变化）引起的动态光谱特征对于样品鉴别更为有用，而 2D-COS 分析是表征温度诱导光谱变化的可靠选择。为了进行评估，在四个不同的温度（20、27、34 和 41°C）下收集了 9 种纯橄榄油和 90 种掺有菜籽油、大豆油和玉米油（掺假率为 5%）的橄榄油的近红外光谱。在恒温测量中，主成分分析（PCA）得到的纯样和掺样的得分明显重叠。当使用温度变化（20-41°C）的光谱进行 2D-COS 分析，并将二维同步相关光谱的结果功率谱用于 PCA 时，两组的识别能力明显增强，随后基于 k-最近邻（k-NN）的判别准确率提高到 86.4%。然而，恒温测量的准确率仅在 50.9%到 55.8%之间。最终，通过 2D-COS 分析有效体现的样品动态温度诱导光谱变化更为有用，并提高了准确性。
